



"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase__              =							{
    '''configuration_roberta''': ['''ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RobertaConfig''', '''RobertaOnnxConfig'''],
    '''tokenization_roberta''': ['''RobertaTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCAmelCase__              =							['''RobertaTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCAmelCase__              =							[
       '''ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''RobertaForCausalLM''',
       '''RobertaForMaskedLM''',
       '''RobertaForMultipleChoice''',
       '''RobertaForQuestionAnswering''',
       '''RobertaForSequenceClassification''',
       '''RobertaForTokenClassification''',
       '''RobertaModel''',
       '''RobertaPreTrainedModel''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCAmelCase__              =							[
       '''TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFRobertaForCausalLM''',
       '''TFRobertaForMaskedLM''',
       '''TFRobertaForMultipleChoice''',
       '''TFRobertaForQuestionAnswering''',
       '''TFRobertaForSequenceClassification''',
       '''TFRobertaForTokenClassification''',
       '''TFRobertaMainLayer''',
       '''TFRobertaModel''',
       '''TFRobertaPreTrainedModel''',
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCAmelCase__              =							[
       '''FlaxRobertaForCausalLM''',
       '''FlaxRobertaForMaskedLM''',
       '''FlaxRobertaForMultipleChoice''',
       '''FlaxRobertaForQuestionAnswering''',
       '''FlaxRobertaForSequenceClassification''',
       '''FlaxRobertaForTokenClassification''',
       '''FlaxRobertaModel''',
       '''FlaxRobertaPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
   from .tokenization_roberta import RobertaTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_roberta_fast import RobertaTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_roberta import (
          ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
          RobertaForCausalLM,
          RobertaForMaskedLM,
          RobertaForMultipleChoice,
          RobertaForQuestionAnswering,
          RobertaForSequenceClassification,
          RobertaForTokenClassification,
          RobertaModel,
          RobertaPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_roberta import (
          TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFRobertaForCausalLM,
          TFRobertaForMaskedLM,
          TFRobertaForMultipleChoice,
          TFRobertaForQuestionAnswering,
          TFRobertaForSequenceClassification,
          TFRobertaForTokenClassification,
          TFRobertaMainLayer,
          TFRobertaModel,
          TFRobertaPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_roberta import (
          FlaxRobertaForCausalLM,
          FlaxRobertaForMaskedLM,
          FlaxRobertaForMultipleChoice,
          FlaxRobertaForQuestionAnswering,
          FlaxRobertaForSequenceClassification,
          FlaxRobertaForTokenClassification,
          FlaxRobertaModel,
          FlaxRobertaPreTrainedModel,
      )

else:
   import sys

   lowerCAmelCase__              =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)









"""simple docstring"""





def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		str     ):







      '''simple docstring'''




      lowerCAmelCase    		=			""""""
      for ch in key:
            if ch == " " or ch not in key_no_dups and ch.isalpha():
                  key_no_dups += ch
      return key_no_dups


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		str     ):







      '''simple docstring'''




      lowerCAmelCase    		=			[chr(i + 65     ) for i in range(26     )]
      # Remove duplicate characters from key
      lowerCAmelCase    		=			remove_duplicates(key.upper()     )
      lowerCAmelCase    		=			len(SCREAMING_SNAKE_CASE     )
      # First fill cipher with key characters
      lowerCAmelCase    		=			{alphabet[i]: char for i, char in enumerate(SCREAMING_SNAKE_CASE     )}
      # Then map remaining characters in alphabet to
      # the alphabet from the beginning
      for i in range(len(SCREAMING_SNAKE_CASE     )				,		26     ):
            lowerCAmelCase    		=			alphabet[i - offset]
            # Ensure we are not mapping letters to letters previously mapped
            while char in key:
                  offset -= 1
                  lowerCAmelCase    		=			alphabet[i - offset]
            lowerCAmelCase    		=			char
      return cipher_alphabet


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		str				,		SCREAMING_SNAKE_CASE	:		dict[str, str]     ):







      '''simple docstring'''




      return "".join(cipher_map.get(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     ) for ch in message.upper()     )


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		str				,		SCREAMING_SNAKE_CASE	:		dict[str, str]     ):







      '''simple docstring'''




      lowerCAmelCase    		=			{v: k for k, v in cipher_map.items()}
      return "".join(rev_cipher_map.get(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     ) for ch in message.upper()     )


def      UpperCAmelCase__				(    ):







      '''simple docstring'''




      lowerCAmelCase    		=			input("""Enter message to encode or decode: """     ).strip()
      lowerCAmelCase    		=			input("""Enter keyword: """     ).strip()
      lowerCAmelCase    		=			input("""Encipher or decipher? E/D:"""     ).strip()[0].lower()
      try:
            lowerCAmelCase    		=			{"""e""": encipher, """d""": decipher}[option]
      except KeyError:
            raise KeyError("""invalid input option"""     )
      lowerCAmelCase    		=			create_cipher_map(SCREAMING_SNAKE_CASE     )
      print(func(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )     )


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       main()






